Trends of Poverty and Income Inequality in Cross-National Comparison
Notice bibliographique
Résumé
Comparative research of poverty, income inequality and the effectiveness of income transfer systems has flourished during the last two decades, largely owing to the contribution of the Luxembourg Income Study project. So far, however, the majority of comparative analyses have been based on a single year. For this paper we analyzed cross-national patterns of poverty and income inequality with a special emphasis on their stability. We studied trends of poverty and income inequality between 1980 and 1995 in nine countries representing three different ideal types of social policy. The differences in poverty across the countries studied corresponded with the respective models of social policy more clearly in the mid-1990s than they did 15 years earlier. Generally speaking, the poverty rate is slightly under 5% in the Nordic countries, around 7.5% in Central Europe, 10% in Canada, 12.5% in the UK, and as high as 17.5% in the USA. All the countries included in the analysis share the trend that the primary distribution - based on the market income - has become less equal than before. In each country, the proportion of population being able to gain subsistence from the market alone has decreased continuously. This trend is significantly more remarkable than the change in actual poverty, which means that the absolute poverty alleviating impact of the income redistribution systems became stronger in these countries during the period 1980-1995. The analysis of income inequality produced a basically similar picture of the differences across the countries and the models of social policy as the analysis of poverty did. In comparison to poverty, however, the change is generally speaking less extensive. The Nordic countries, in particular, have been capable of responding to the rise of the market income differences so that the income inequality for disposable incomes has practically not increased at all. Canada shows a parallel trend. The USA and, in particular, the UK represent the opposite development. We also analyzed trends of poverty in various population groups. It was found that by 1995 poverty had turned into a risk of young adults in all the countries studied. The poverty rate increased for the age group 18-30 years in all countries, while an opposite trend was observed among the elderly, in particular those aged over 65. Poverty rate among the elderly is nowadays below the average population-level rate in all the countries studied.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».